Modeling the Spatio-Temporal Annual Changes in Human Tick-Borne Encephalitis (TBE) Risk in Europe

Introduction: Caused by the tick-borne encephalitis virus (TBEV), tick-borne encephalitis (TBE) is a zoonotic disease that can cause severe neurological symptoms. Despite the availability of a vaccine, it remains a public health concern in Europe, with an increasing number of reported human cases and new hotspots of virus circulation, also in previously non-endemic areas. To geolocate and predict new areas at risk of human TBE infections, we developed a spatio-temporal predictive model to infer the year-to-year probability of human TBE occurrence across Europe at the regional and municipal administrative levels. Methods: We derived the distribution of human TBE cases at the regional (NUTS-3) level during the period 2017-2022 using data provided by the European surveillance system (TESSy, ECDC), while the distribution of human TBE cases at the municipal level during the same years was obtained using data from five European countries (Austria, Finland, Italy, Lithuania, and Slovakia). We modelled the probability of TBE occurrence at regional and municipal levels for the period 2017-2024 using a boosted regression trees approach, including both hazard and exposure variables affecting TBE risk: climate, land cover, presence of tick hosts to account for the natural hazard of virus circulation, forest road density and human population density as proxies for the probability of human exposure to tick bites. Results: Our modelling framework provides a multi-scale approach to predict yearly variations in the risk of occurrence of human TBE cases in Europe. Our results highlight a significant rising trend in the probability of human infection with TBE not only in north-western, but also in south-western European countries and show that areas at high risk of TBE are characterized by the presence of key tick host species, intense human recreational activity in forests, steep drops in late summer temperatures and high annual precipitation. Discussion: Our study provides a modelling framework for the early annual assessment and identification of European regions and municipalities at risk of human TBE infection, based on covariates reflecting both the hazard and exposure dimensions. Being based on lagged covariates, our approach can also be used to predict risk areas one year in advance, thus supporting surveillance, prevention, and control of human TBE infections by public health authorities. ### Competing Interest Statement The authors have declared no competing interest.